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💠 Compositional Learning Journal Club

Join us this week for an in-depth discussion on Compositional Learning in the context of cutting-edge text-to-image generative models. We will explore recent breakthroughs and challenges, focusing on how these models handle compositional tasks and where improvements can be made.

✅ This Week's Presentation:

🔹 Title: GenArtist: Multimodal LLM as an Agent for Unified Image Generation and Editing

🔸 Presenter: Dr Rohban

🌀 Abstract:
This innovative framework addresses the limitations of current image generation models in handling intricate text prompts and ensuring reliability through verification and self-correction mechanisms. Coordinated by a multimodal large language model (MLLM) agent, GenArtist integrates a diverse library of tools, enabling seamless task decomposition, step-by-step execution, and systematic self-correction. With its tree-structured planning and advanced use of position-related inputs, GenArtist achieves state-of-the-art performance, outperforming models like SDXL and DALL-E 3. This session will delve into the system’s architecture and its groundbreaking potential for advancing image generation and editing tasks.


📄 Papers: GenArtist: Multimodal LLM as an Agent for Unified Image Generation and Editing


Session Details:
- 📅 Date: Wednesday
- 🕒 Time: 3:30 - 4:30 PM
- 🌐 Location: Online at vc.sharif.edu/ch/rohban

We look forward to your participation! ✌️
arXiv.org
GenArtist: Multimodal LLM as an Agent for Unified Image Generation...
Despite the success achieved by existing image generation and editing methods, current models still struggle with complex problems including intricate text prompts, and the absence of verification...
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💠 Compositional Learning Journal Club

Join us this week for an in-depth discussion on Compositional Learning in the context of cutting-edge text-to-image generative models. We will explore recent breakthroughs and challenges, focusing on how these models handle compositional tasks and where improvements can be made.

✅ This Week's Presentation:

🔹 Title: GenArtist: Multimodal LLM as an Agent for Unified Image Generation and Editing

🔸 Presenter: Dr Rohban

🌀 Abstract:
This innovative framework addresses the limitations of current image generation models in handling intricate text prompts and ensuring reliability through verification and self-correction mechanisms. Coordinated by a multimodal large language model (MLLM) agent, GenArtist integrates a diverse library of tools, enabling seamless task decomposition, step-by-step execution, and systematic self-correction. With its tree-structured planning and advanced use of position-related inputs, GenArtist achieves state-of-the-art performance, outperforming models like SDXL and DALL-E 3. This session will delve into the system’s architecture and its groundbreaking potential for advancing image generation and editing tasks.


📄 Papers: GenArtist: Multimodal LLM as an Agent for Unified Image Generation and Editing


Session Details:
- 📅 Date: Wednesday
- 🕒 Time: 3:30 - 4:30 PM
- 🌐 Location: Online at vc.sharif.edu/ch/rohban

We look forward to your participation! ✌️

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💠 Compositional Learning Journal ClubJoin us this week for an in-depth discussion on Compositional Learning in the context of cutting-edge text-to-image generative models. We will explore recent breakthroughs and challenges

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